Project Summary/Abstract Positron emission tomography combined with computed tomography (PET/CT) using the radiolabeled tracer 2- deoxy-2-(18F)fluoro-D-glucose (FDG) has become a standard imaging tool for cancer patient management. The semi-quantitative parameter standardized uptake value (SUV) is routinely used in clinical for tumor uptake quantification, which is computed on the static PET image acquired at a certain time (typically 60 min) post tracer injection for a short interval (typically 5-15 min). However, the quantification accuracy of SUV from a single PET scan suffers from the variabilities of tracer plasma clearance and acquisition start time. The dual- time-point FDG PET imaging has been intensively investigated and used in both clinical and research studies, typically one scan at 60 min and the other at 120 min, showing the potential to enhance the diagnostic accuracy of FDG PET by differentiating malignancy from inflammation and normal tissue. However, the current clinical dual-time-point FDG PET studies use the relative SUV change between two scans as the quantification index, which cannot eliminate the variations in tracer plasma clearance. Meanwhile, the dual-time-point protocol has not been optimized and standardized currently, leading to conflicting results. The fully-quantitative parameter, tracer net uptake rate constant Ki, is the most accurate parameter to quantify FDG PET, which is calculated using dynamic imaging with compartmental modeling. Ki is independent on the plasma clearance or acquisition start time. However, the long and complex acquisition protocol (typically at least 60 min), which requires dynamic scanning and sequential arterial blood sampling (or image-derived blood activity) used as input function from the time of injection, limits its application in clinical practice. Meanwhile, generation of the parametric Ki image, which can provide additional heterogeneity information for FDG PET, is challenging clinically using voxel-by-voxel compartmental modeling due to the computational cost and being sensitive to noise using non-linear least squares. The graphical Patlak plot, can be used for simplified Ki calculation and Ki image generation by voxel-by-voxel fitting. However, it still needs dynamic scanning starting from 15-30 min after injection and input function from the time of injection. The aims of this proposal are 1) to optimize the dual-time-point protocol for accurate Ki quantification using Patlak plot without the need for individual patient's input function, and 2) to generate high-quality low-noise dual-time-point Ki images using novel techniques based on deep learning. Upon the success of this project, our proposed approach can obtain reliable tumor Ki quantification and parametric Ki image for free without adding any additional complexity on the existing dual- time-point protocol currently used in clinical practice, with great potential of improving diagnosis and therapy assessment in oncology. We expect the translation of this approach to clinical investigation to be fast, as this is a post-processing approach and is based on data already acquired using clinically used protocol without imposing additional burden to technologists.